Founding Full-Stack Engineer

Instaffo GmbH

München

Hybrid

EUR 75,000 - 120,000

Full time

44 hours ago
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Benefits offered by this job

Equity 0.5%–1.5% (four-year vest, 1.5‑
Hybrid work in Munich
AI tooling stipend €500/month

Job summary

Alago, a Munich-based AI startup for the construction industry, is seeking a strong full‑stack engineer. You will shape dashboards, task views, and alerts with TypeScript and React/Next.js, while crafting robust back-end APIs and a scalable data layer for highly interconnected construction data.

The role emphasizes rapid delivery, reliability on live projects, and collaboration with founders to influence product direction.

Qualifications

  • Strong full-stack in TypeScript and React/Next.js.
  • Experience designing schemas and optimizing queries for interconnected data.
  • Built and consumed REST APIs with authentication and rate limiting.
  • Ability to reason from user pain to measurable outcomes and discuss with non-technical customers.
  • Willingness to move fast while maintaining reliability on live projects.

Responsibilities

  • Build AI-native project management dashboards, views, and alerts.
  • Design the data layer for interconnected construction data and APIs.
  • Develop production surfaces where automation runs and recommendations surface for approval.
  • Prototype rapidly and measure outcomes while ensuring reliability on live projects.

Skills

Full-stack
TypeScript
React/Next.js
Backend APIs
Database design
REST APIs
Product sense
Startup mindset
Reliability

Tools

LangChain
Vercel
Supabase
Postgres
pgvector
LangFuse
shadcn/ui

Job description

Requirements

Please note that this position requires work authorization for Germany.

Language: English - Business fluent (C2).

Must-haves
  • Strong full-stack in TypeScript and React/Next.js. You build complex, performant frontends and the backend APIs behind them.
  • Database design and optimization. You've designed schemas, optimized queries, and worked with complex data relationships. Construction data is deeply interconnected.
  • API design. You've built and consumed REST APIs, handled auth and rate limiting, and designed interfaces that scale with the product.
  • Product sense. You reason from user pain to solution to measurable outcome, and you can talk to non-technical customers and understand their workflows.
  • Startup pace with judgment. You turn feedback into shipped features fast and make pragmatic architecture calls, but you care about reliability because wrong has consequences on a live construction project.
Nice-to-haves
  • Comfort integrating AI features, even if you don't build the models yourself.
  • Startup experience, ideally as an early employee with a broad remit.
  • Domain depth in construction, manufacturing, or complex B2B workflows.
  • Design sense. You can build a clean, usable interface without detailed mockups.
  • Open-source work or writing you're proud of.
  • German for customer conversations. Not required, we have native speakers for that.
Activities

The construction industry loses $1.6 trillion a year to inefficiency, and most of it is avoidable. The knowledge that would prevent it already exists. It's just trapped in PDFs, meeting protocols, and email threads nobody can search. Every new project relearns what the last one already knew.

We're building the opposite of that: software for the people who run large, complex construction projects. It reads the documents, tracks the decisions, and catches the mistakes early. The model isn't the moat. The project memory is, and it compounds: every project makes the system better, and a competitor starting today is already years of projects behind.

Our software runs today on a live autobahn construction program and an S-Bahn transit program: multi-year timelines, hundreds of thousands of pages of specs, protocols, and site communications, and real consequences when we get an answer wrong.

We're pre-seed, led by Realyze Ventures, whose LPs include Zech and other large European construction groups. Co-investors: D11Z, the family office behind Aleph Alpha, and the CDTM Venture Fund, backed by 300+ CDTM alumni including the founders of Personio and Alasco and DeepMind's Technical Director. 25+ live customers.

What You'd Work On

Two hard problems, both in production.

AI-native project management, not a chatbot on PDFs.

You build the product project managers live in every day: the dashboards, task views, and alerts. The AI belongs in the background, not in a sidebar you talk to, so you're building surfaces where automation runs on cronjobs, recommendations surface for the PM to approve, and some tasks run end to end with the user only reviewing after.

The data layer under a five-year project.

Construction data is deeply interconnected: documents, protocols, decisions, tasks, and stakeholders all cross-reference each other across timelines that run for years. You design the schema, write the queries, and build the APIs that hold that graph and still serve the product fast enough to feel instant, at hundreds of thousands of pages and 50 stakeholders per project.

Stack.

TypeScript, Next.js, Vercel, Supabase (Postgres + pgvector), LangChain, Vercel AI SDK, LangFuse, shadcn/ui. No legacy. Greenfield.

How We Work
  • We ship at 80% and improve in the next iteration. Our users are on live infrastructure projects, so we learn from production.
  • We'd rather you read the paper than reach for the nearest library. Most of what we build doesn't have a library yet.
  • Feedback is direct and it's about the work. We move too fast to be precious about it.
  • High ownership. If you spot the problem, it's yours to fix.
Who We're Looking For

You reason from user pain to solution to measurable outcome, and you can sit across from a non-technical customer and understand how they actually work.

You're a strong full-stack engineer. You build complex, performant frontends in TypeScript and React/Next.js and the backend APIs behind them, and you have real instincts for database design and query optimization, because construction data is deeply interconnected.

You prototype fast and measure everything. You also care about reliability, because on a live construction project, wrong has consequences.

Level is open. We've watched new grads outrun staff engineers and the other way around. If you're exceptional, we'll build the scope around you.

Nice to have, not required: comfort integrating AI features even if you don't build the models yourself, strong open-source work, an earlier stint as an early employee at a startup, domain depth in construction or complex B2B workflows, or a good eye for design so you can build clean interfaces without detailed mockups.

We work in English. German helps for customer calls but isn't required. We have native speakers for that.

The offer

You'll be one of our first engineers, which means you own critical parts of the product end to end and work directly with the founders. It's early enough that you help decide how the company works, and the path from here goes to either top individual contributor or VP Engineering, depending on where you're strongest.

  • Equity: 0.5% to 1.5%, four-year vest, 1.5-year cliff.
  • Hybrid: three days a week in our central Munich office. If that's a dealbreaker for you, let's talk.
  • Every engineer gets €500/month for AI tooling: Claude Code, Cursor background agents, and whatever frontier model you want to test.
Team

You'd join a small team that works directly with the founders, where everyone owns real problems from the first week. We ship the 80% version today instead of the perfect one next month, test ideas early rather than argue about them, and keep feedback direct and about the work. When something goes right, we actually stop and notice it.

About The Company

Alago is a Munich-based AI startup for the construction industry. With over €1M in funding, we're building an AI platform that catches mistakes in construction projects early and brings transparency to complex project workflows.

We're already live with 30+ customers, helping project teams automate time-consuming routine work, reduce sources of error, and turn the lessons of past projects into lasting value for future ones.

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